Moh. Zulfiqar Naufal Maulana, Daniel Siahaan, Ahmad Saikhu, Evi Triandini
Software requirement prioritization is a critical step in software development, as it directly impacts project efficiency and success. Despite the availability of various prioritization techniques, there remains a gap in directly comparing optimization algorithms for this purpose. This study addresses this gap by evaluating three widely-used metaheuristic-based optimization algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO). Using a dataset enriched with attributes such as cost, value, prerequisites, and test cases, the algorithms were analyzed based on accuracy, execution time, and computational complexity. The results show that GA and PSO demonstrate comparable accuracy (82%) and efficiency, with GA exhibiting faster execution time (0.822s) compared to PSO (1.324s). GWO, while slightly less accurate (81%) and slower (3.017s), offers unique advantages in specific optimization contexts. This study provides actionable insights for software engineers in selecting appropriate algorithms to optimize resource allocation and enhance decision-making during requirement prioritization. © 2024 IEEE.
Institut Teknologi Sepuluh Nopember, Department of Informatics, Surabaya, Indonesia; Universitas Negeri Malang, Department of Electrical Engineering and Informatics, Malang, Indonesia; Institut Teknologi dan Bisnis STIKOM Bali, Department of Information System, Denpasar, Indonesia
